GitHub Enhances Copilot with Custom Model for Improved Code Completions

Luisa Crawford
Oct 24, 2025 17:54

GitHub introduces a new custom model for Copilot, enhancing code completion speed and accuracy, with a focus on developer feedback and real-world usage.

GitHub has unveiled a new custom model designed to enhance its AI-powered coding assistant, Copilot. The latest updates promise faster, smarter code completions, with improvements driven by extensive developer feedback, according to a post by Shengyu Fu and John Mogensen on the GitHub Blog.

Enhancements in Code Completion

The updates to GitHub Copilot focus on delivering more relevant and efficient code suggestions. These improvements include a 20% increase in accepted and retained characters, a 12% higher acceptance rate, and a threefold increase in token-per-second throughput, coupled with a 35% reduction in latency. These changes aim to enhance the overall experience across various editors and environments, allowing developers to spend less time editing and more time building.

Why It Matters

The focus on optimizing for accepted and retained characters, alongside code flow, marks a shift from the previous emphasis on acceptance rates alone. By doing so, GitHub aims to provide suggestions that developers find more useful and relevant, ultimately enhancing productivity. The updated model ensures that a greater portion of Copilot’s suggestions remain in the final code, thus reducing unnecessary keystrokes.

Evaluation and Feedback

To ensure the effectiveness of the new model, GitHub relied on a multi-layered evaluation strategy. This included offline, pre-production, and production evaluations, each contributing to refining different aspects of the code completion experience. The model’s performance is assessed through metrics like accepted-and-retained characters, acceptance rates, and latency, ensuring real-world applicability and developer satisfaction.

Training the Custom Model

The training process for the new model involved mid-training on a curated corpus of modern code, followed by supervised fine-tuning and reinforcement learning. This approach ensured the model’s fluency, consistency in style, and awareness of context. The reinforcement learning algorithm focused on enhancing code quality, relevance, and helpfulness, resulting in completions that are more precise and useful for developers.

Future Developments

Looking ahead, GitHub plans to expand Copilot’s capabilities into domain-specific areas such as game engines and financial systems. The team is also working on refining reward functions to further improve the quality and relevance of code completions, ensuring that Copilot continues to offer high-quality assistance in diverse developer environments.

The enhancements to GitHub Copilot underscore the platform’s commitment to leveraging AI to improve developer productivity and streamline the coding process. By integrating developer feedback and focusing on real-world application, GitHub aims to offer a more intuitive and effective coding assistant.

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